The lateral geniculate nucleus (LGN) is classically portrayed as a relay station that simply serves to transfer signals from the retina to the primary visual cortex. According to this account, the LGN passively provides the necessary feedforward input to the visual cortex, but has no direct involvement in more complex perceptual processes. However, such an account fails to explain why the LGN receives far more afferents from the visual cortex than from the retina; moreover, it ignores the possibility that top-down feedback signals from the visual cortex to the LGN could have an important role in perceptual coding and in shaping the complex topography of responses that arise from the early visual system. According to neural theories of predictive coding, neurons in higher visual areas with large receptive fields can process more global information and send predictions about the input they receive to the lower visual area providing input. Any local errors in these globally informed predictions are then computed as residual error signals in the lower area. According to this account, portions of a visual scene that appear irregular or less expected, such as a figure that differs in featural content from its surround, may be highlighted at this lower site by additional residual processing. A far-reaching implication of this theory is that these top-down predictions may propagate to the lowest possible site of the visual hierarchy, modulating the response of the LGN to figural regions that differ in appearance from the adjacent background. This project will provide a novel evaluation of the functional role of the LGN in figure-ground processing, characterizing the impact of feedback modulation at the earliest possible site of the human visual pathway. We will use high-resolution fMRI at 7 Tesla to investigate multiple aspects of figure-ground processing in the LGN and V1. In Specific Aim 1, we will determine whether figure-selective enhancement in the early visual system depends on automatic perceptual processes or a mechanism of spatial attentional feedback. In Specific Aim 2, we will apply population coding models and multivariate regression techniques to characterize the spatial profile of figure-ground processes in the LGN and V1, and test for distinct mechanisms of boundary detection and figure enhancement. In Specific Aim 3, we will evaluate whether modulatory figure-ground effects in the LGN can be attributed to top-down feedback from binocularly sensitive visual cortex, and provide fine-grained characterization of the tuning profile of this feedback modulation. The results of this project will provide new insights into the perceptual functions of the human LGN, which are poorly understood, and yield critical new data to inform current models of predictive coding and figure-ground processing. The development of high- resolution fMRI methods to characterize and reconstruct LGN and V1 responses in image space is also of considerable health relevance. Future applications of this approach could be used to construct detailed visual- field maps of LGN and V1 responses associated with damage to the peripheral retina, impairments of central visual processing such as amblyopia, as well as the impact of clinical interventions.